Yho contributed to both the AI-Hypercomputer/torchprime and pytorch/xla repositories, focusing on distributed training reliability, documentation, and test stability. He developed a diagnostics toolkit in Python to validate distributed training setups, logging environment details and integrating PyTorch and XLA checks, while also authoring comprehensive troubleshooting guides. Yho centralized performance metrics tracking by migrating results to a CSV-based system and updated related documentation for reproducibility. He improved code quality through targeted refactoring for readability and maintainability, and stabilized gradient checkpointing tests by adjusting tolerances to account for XLA optimizations, ultimately enhancing developer experience and reducing maintenance overhead across both projects.